
'''simple docstring'''



import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import BridgeTowerImageProcessor




class     UpperCAmelCase_	(						unittest.TestCase					):






						'''simple docstring'''






						def __init__(   self       ,  _lowercase       ,  _lowercase = True       ,  _lowercase = None       ,  _lowercase = 32       ,  _lowercase = True       ,  _lowercase = 1 / 255       ,  _lowercase = True       ,  _lowercase = True       ,  _lowercase = [0.4814_5466, 0.457_8275, 0.4082_1073]       ,  _lowercase = [0.2686_2954, 0.2613_0258, 0.2757_7711]       ,  _lowercase = True       ,  _lowercase=7       ,  _lowercase=30       ,  _lowercase=400       ,  _lowercase=3       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					parent
												_lowerCAmelCase    		=					do_resize
												_lowerCAmelCase    		=					size if size is not None else {"""shortest_edge""": 288}
												_lowerCAmelCase    		=					size_divisor
												_lowerCAmelCase    		=					do_rescale
												_lowerCAmelCase    		=					rescale_factor
												_lowerCAmelCase    		=					do_normalize
												_lowerCAmelCase    		=					do_center_crop
												_lowerCAmelCase    		=					image_mean
												_lowerCAmelCase    		=					image_std
												_lowerCAmelCase    		=					do_pad
												_lowerCAmelCase    		=					batch_size
												_lowerCAmelCase    		=					num_channels
												_lowerCAmelCase    		=					min_resolution
												_lowerCAmelCase    		=					max_resolution




						def    _lowercase      (   self ):

												"""simple docstring"""



												return {
												    "image_mean": self.image_mean,
												    "image_std": self.image_std,
												    "do_normalize": self.do_normalize,
												    "do_resize": self.do_resize,
												    "size": self.size,
												    "size_divisor": self.size_divisor,
												}




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase=False ):

												"""simple docstring"""



												if not batched:
																		_lowerCAmelCase    		=					self.size["""shortest_edge"""]
																		_lowerCAmelCase    		=					image_inputs[0]
																		if isinstance(_lowercase       ,  Image.Image ):
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					image.size
																		else:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					image.shape[1], image.shape[2]
																		_lowerCAmelCase    		=					size / min(_lowercase       ,  _lowercase )
																		if h < w:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					size, scale * w
																		else:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					scale * h, size

																		_lowerCAmelCase    		=					int((1_333 / 800) * size )
																		if max(_lowercase       ,  _lowercase ) > max_size:
																								_lowerCAmelCase    		=					max_size / max(_lowercase       ,  _lowercase )
																								_lowerCAmelCase    		=					newh * scale
																								_lowerCAmelCase    		=					neww * scale

																		_lowerCAmelCase		,       _lowerCAmelCase    		=					int(newh + 0.5 ), int(neww + 0.5 )
																		_lowerCAmelCase		,       _lowerCAmelCase    		=					(
																		    newh // self.size_divisor * self.size_divisor,
																		    neww // self.size_divisor * self.size_divisor,
																		)

												else:
																		_lowerCAmelCase    		=					[]
																		for image in image_inputs:
																								_lowerCAmelCase		,       _lowerCAmelCase    		=					self.get_expected_values([image] )
																								expected_values.append((expected_height, expected_width) )
																		_lowerCAmelCase    		=					max(_lowercase       ,  key=lambda _lowercase : item[0] )[0]
																		_lowerCAmelCase    		=					max(_lowercase       ,  key=lambda _lowercase : item[1] )[1]

												return expected_height, expected_width






@require_torch
@require_vision
class     UpperCAmelCase_	(						_SCREAMING_SNAKE_CASE			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: Optional[int]										=  BridgeTowerImageProcessor if is_vision_available() else None




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					BridgeTowerImageProcessingTester(self )




						@property
						def    _lowercase      (   self ):

												"""simple docstring"""



												return self.image_processor_tester.prepare_image_processor_dict()




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.image_processing_class(**self.image_processor_dict )
												self.assertTrue(hasattr(_lowercase       ,  """image_mean""" ) )
												self.assertTrue(hasattr(_lowercase       ,  """image_std""" ) )
												self.assertTrue(hasattr(_lowercase       ,  """do_normalize""" ) )
												self.assertTrue(hasattr(_lowercase       ,  """do_resize""" ) )
												self.assertTrue(hasattr(_lowercase       ,  """size""" ) )
												self.assertTrue(hasattr(_lowercase       ,  """size_divisor""" ) )




						def    _lowercase      (   self ):

												"""simple docstring"""



												pass




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.image_processing_class(**self.image_processor_dict )
												# create random PIL images
												_lowerCAmelCase    		=					prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_lowercase )
												for image in image_inputs:
																		self.assertIsInstance(_lowercase       ,  Image.Image )

												# Test not batched input
												_lowerCAmelCase    		=					image_processing(image_inputs[0]       ,  return_tensors="""pt""" ).pixel_values

												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.image_processor_tester.get_expected_values(_lowercase )
												self.assertEqual(
												    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

												# Test batched
												_lowerCAmelCase    		=					image_processing(_lowercase       ,  return_tensors="""pt""" ).pixel_values

												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.image_processor_tester.get_expected_values(_lowercase       ,  batched=_lowercase )
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,  )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.image_processing_class(**self.image_processor_dict )
												# create random numpy tensors
												_lowerCAmelCase    		=					prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_lowercase       ,  numpify=_lowercase )
												for image in image_inputs:
																		self.assertIsInstance(_lowercase       ,  np.ndarray )

												# Test not batched input
												_lowerCAmelCase    		=					image_processing(image_inputs[0]       ,  return_tensors="""pt""" ).pixel_values

												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.image_processor_tester.get_expected_values(_lowercase )
												self.assertEqual(
												    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

												# Test batched
												_lowerCAmelCase    		=					image_processing(_lowercase       ,  return_tensors="""pt""" ).pixel_values

												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.image_processor_tester.get_expected_values(_lowercase       ,  batched=_lowercase )
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,  )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.image_processing_class(**self.image_processor_dict )
												# create random PyTorch tensors
												_lowerCAmelCase    		=					prepare_image_inputs(self.image_processor_tester       ,  equal_resolution=_lowercase       ,  torchify=_lowercase )
												for image in image_inputs:
																		self.assertIsInstance(_lowercase       ,  torch.Tensor )

												# Test not batched input
												_lowerCAmelCase    		=					image_processing(image_inputs[0]       ,  return_tensors="""pt""" ).pixel_values

												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.image_processor_tester.get_expected_values(_lowercase )
												self.assertEqual(
												    encoded_images.shape       ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,  )

												# Test batched
												_lowerCAmelCase    		=					image_processing(_lowercase       ,  return_tensors="""pt""" ).pixel_values

												_lowerCAmelCase		,       _lowerCAmelCase    		=					self.image_processor_tester.get_expected_values(_lowercase       ,  batched=_lowercase )
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.image_processor_tester.num_channels,
												        expected_height,
												        expected_width,
												    )       ,  )

'''simple docstring'''




import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
__UpperCAmelCase					=pytest.mark.integration


__UpperCAmelCase					={"comet"}
__UpperCAmelCase					=importlib.util.find_spec("fairseq") is not None

__UpperCAmelCase					={"code_eval"}
__UpperCAmelCase					=os.name == "nt"

__UpperCAmelCase					={"bertscore", "frugalscore", "perplexity"}
__UpperCAmelCase					=importlib.util.find_spec("transformers") is not None






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Any:
    @wraps(UpperCamelCase__							)
    def wrapper(self							,    UpperCamelCase__							):
        if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
            self.skipTest('''"test requires Fairseq"'''							)
        else:
            test_case(self							,    UpperCamelCase__							)

    return wrapper






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Any:
    @wraps(UpperCamelCase__							)
    def wrapper(self							,    UpperCamelCase__							):
        if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
            self.skipTest('''"test requires transformers"'''							)
        else:
            test_case(self							,    UpperCamelCase__							)

    return wrapper






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[Any]:
    @wraps(UpperCamelCase__							)
    def wrapper(self							,    UpperCamelCase__							):
        if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
            self.skipTest('''"test not supported on Windows"'''							)
        else:
            test_case(self							,    UpperCamelCase__							)

    return wrapper






def __lowerCAmelCase					(				)      ->       Any:
    __lowerCamelCase            =    [metric_dir.split(os.sep							)[-2] for metric_dir in glob.glob('''./metrics/*/'''							)]
    return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished




@parameterized.named_parameters(get_local_metric_names()					)
@for_all_test_methods(
    UpperCAmelCase__				,  UpperCAmelCase__				,  UpperCAmelCase__					)
@local
class        a__ (  parameterized.TestCase					):
     lowerCamelCase    :	str				={}
     lowerCamelCase    :	Union[str, Any]				=None






     @pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''       )
     @pytest.mark.filterwarnings('''ignore:load_metric is deprecated:FutureWarning'''       )
     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple    ,							a						:						List[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    '''[...]'''
         __lowerCamelCase            =    importlib.import_module(
             datasets.load.metric_module_factory(os.path.join('''metrics'''    ,							a       )       ).module_path       )
         __lowerCamelCase            =    datasets.load.import_main_class(metric_module.__name__    ,							dataset=a       )
         # check parameters
         __lowerCamelCase            =    inspect.signature(metric._compute       ).parameters
         self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()       )       )  # no **kwargs
         # run doctest
         with self.patch_intensive_calls(a    ,							metric_module.__name__       ):
             with self.use_local_metrics():
                 try:
                     __lowerCamelCase            =    doctest.testmod(a    ,							verbose=a    ,							raise_on_error=a       )
                 except doctest.UnexpectedException as e:
                     raise e.exc_info[1]  # raise the exception that doctest caught
         self.assertEqual(results.failed    ,							0       )
         self.assertGreater(results.attempted    ,							1       )






     @slow
     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    '''[...]'''
         __lowerCamelCase            =    importlib.import_module(
             datasets.load.metric_module_factory(os.path.join('''metrics'''    ,							a       )       ).module_path       )
         # run doctest
         with self.use_local_metrics():
             __lowerCamelCase            =    doctest.testmod(a    ,							verbose=a    ,							raise_on_error=a       )
         self.assertEqual(results.failed    ,							0       )
         self.assertGreater(results.attempted    ,							1       )






     @contextmanager
     def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							a						:						Optional[Any]    ,							a						:						Any       ):





         """simple docstring"""



         if metric_name in self.INTENSIVE_CALLS_PATCHER:
             with self.INTENSIVE_CALLS_PATCHER[metric_name](a       ):
                 yield
         else:
             yield






     @contextmanager
     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         def load_local_metric(a						:						List[str]    ,							*a						:						Optional[int]    ,							**a						:						Tuple       ):
             return load_metric(os.path.join('''metrics'''    ,							a       )    ,							*a    ,							**a       )

         with patch('''datasets.load_metric'''       ) as mock_load_metric:
             __lowerCamelCase            =    load_local_metric
             yield






     @classmethod
     def   SCREAMING_SNAKE_CASE__  (					cls						:						List[str]    ,							a						:						Tuple       ):





         """simple docstring"""



         def wrapper(a						:						List[Any]       ):
             __lowerCamelCase            =    contextmanager(a       )
             __lowerCamelCase            =    patcher
             return patcher

         return wrapper






@LocalMetricTest.register_intensive_calls_patcher('''bleurt'''							)
def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Union[str, Any]:
    import tensorflow.compat.va as tf
    from bleurt.score import Predictor

    tf.flags.DEFINE_string('''sv'''							,    ''''''							,    ''''''							)  # handle pytest cli flags




    class        a__ (  UpperCAmelCase__					):






         def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple    ,							a						:						Dict       ):





             """simple docstring"""



             assert len(input_dict['''input_ids''']       ) == 2
             return np.array([1.03, 1.04]       )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
    with patch('''bleurt.score._create_predictor'''							) as mock_create_predictor:
        __lowerCamelCase            =    MockedPredictor()
        yield






@LocalMetricTest.register_intensive_calls_patcher('''bertscore'''							)
def __lowerCAmelCase					(				UpperCamelCase__							)      ->       Tuple:
    import torch

    def bert_cos_score_idf(UpperCamelCase__							,    UpperCamelCase__							,    *UpperCamelCase__							,    **UpperCamelCase__							):
        return torch.tensor([[1.0, 1.0, 1.0]] * len(UpperCamelCase__							)							)

    # mock get_model which is supposed to do download a bert model
    # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
    with patch('''bert_score.scorer.get_model'''							), patch(
        '''bert_score.scorer.bert_cos_score_idf'''							) as mock_bert_cos_score_idf:
        __lowerCamelCase            =    bert_cos_score_idf
        yield






@LocalMetricTest.register_intensive_calls_patcher('''comet'''							)
def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:
    def load_from_checkpoint(UpperCamelCase__							):




        class        a__ :






             def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]    ,							a						:						str    ,							*a						:						int    ,							**a						:						Tuple       ):





                 """simple docstring"""



                 assert len(a       ) == 2
                 __lowerCamelCase            =    [0.19, 0.92]
                 return scores, sum(a       ) / len(a       )
        return Model()

    # mock load_from_checkpoint which is supposed to do download a bert model
    # mock load_from_checkpoint which is supposed to do download a bert model
    with patch('''comet.download_model'''							) as mock_download_model:
        __lowerCamelCase            =    None
        with patch('''comet.load_from_checkpoint'''							) as mock_load_from_checkpoint:
            __lowerCamelCase            =    load_from_checkpoint
            yield






def __lowerCAmelCase					(				)      ->       List[Any]:
    __lowerCamelCase            =    load_metric(os.path.join('''metrics'''							,    '''seqeval'''							)							)
    __lowerCamelCase            =    '''ERROR'''
    __lowerCamelCase            =    f"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
    with pytest.raises(UpperCamelCase__							,    match=re.escape(UpperCamelCase__							)							):
        metric.compute(predictions=[]							,    references=[]							,    scheme=UpperCamelCase__							)
